where \(r\) , \(g\) , \(b\)  denote image channel, \(u\) , \(v\)  denote the spatial location. We pad the input image with one row on the top and one column on the left to initiate the prediction.
For the red channel, the current point is predicted by its up, left, upper-left points like Paeth filter. Inspired by YCoCg-R color space which is shown successful in FLIF[1]} format and utilizes different channels to calculate the color space, for green and blue channel, we also use their previous channel to conduct the prediction.
Different from [2]}, our AR module models the pictures channel-wise, predict the mean only, and only has 12 parameters (with a bias in each channel), but it could still generate a high-quality prediction.
As shown in  fig:framework, \(\hat{x}\)  is quite close to \(x\) , which suggests the locality of natural images.
